1. Introduction
Rheumatoid arthritis is a health condition that is characterized by the inflammation of joints that can limit an individual’s range of motion. In the United States, approximately 54 million individuals are diagnosed with arthritis, and among them, 24 million had activity limitations attributable to the condition [
1]. Arthritis typically develops with age, and approximately 50% of individuals over the age of 65 have been diagnosed with arthritis [
1]. Severe arthritis may increase the driver’s crash risk due to a reduced range of joint motion and pain. The high prevalence of arthritis among the driver population is a safety and public health concern, especially among senior drivers. There is a need to understand the impacts of arthritis on crash risk and driving behavior because this understanding will provide critical information for the development of assistance systems that improve the safety and mobility of drivers with arthritis.
Scholars have shown that individuals with rheumatoid arthritis could have deteriorated driving performance and difficulty in driving [
2,
3]. Jones et al. [
4] indicated that drivers with rheumatoid arthritis and osteoarthritis had difficulty conducting common driving tasks, including buckling the seat belt, looking over one’s shoulder to check blind spots, turning the wheel, and using the hand brake. Similarly, Vrkljan et al. [
5] showed that drivers with arthritis experienced problems that could affect safety. However, the authors of these studies also suggest that the majority of the difficulties with the driving task would not necessarily affect safety. Proper techniques, such as threading steering and backing up using mirrors can effectively mitigate the impairment associated with arthritis [
4].
Relatively few studies have directly linked arthritis with crash risk, and inconsistent results have been reported. McGwin et al. [
6] conducted a population-based case-control study of chronic medical conditions and crashes among older drivers. The study found that arthritis was associated with an increased risk among females (odds ratio [OR] = 1.8) but not among males (OR = 0.8). A 5-year cohort study (1991–1995) by Sims et al. [
7] based on police-reported crashes in Alabama for 174 senior drivers (>55 years old) revealed that there is no association between arthritis and crashes. Similarly, in a cohort study of 1791 older drivers, Foley et al. [
8] found no association between arthritis and crash involvement. The inconsistency in the relevant literature suggests that in-depth studies are needed to fully understand the safety impacts of arthritis.
Fitness-to-drive has been a major safety and mobility concern for senior drivers, and it can be directly affected by arthritis [
9]. Researchers have shown that the fitness abilities of senior drivers can affect both driving safety and the decision to give up driving. Guo et al. [
10] showed that fitness-to-drive is a key reason that seniors give up driving. Using naturalistic driving study (NDS) data, Antin et al. [
11] showed that certain fitness measures, including upper body strength, are associated with increased crash risk. Because drivers with arthritis or other fitness conditions tend to drive less than others, arthritis could be a factor underlying the well-documented low-mileage-bias (LMB) issue for senior drivers. The LMB refers to the phenomenon that senior drivers with lower annualized mileage are at significantly higher risk compared to drivers with higher annualized mileage [
12]. The connection between arthritis and these known risk factors can help shed light on the causal path for arthritis and crashes.
Arthritis can also affect driver behavior, such as cellphone use and other secondary tasks. Secondary task engagement, which has been defined as “attention given to non-driving-related activity, typically to the detriment of driving performance” [
13], has been shown to be a critical reason for crashes, causing about 1.2 million crashes and contributing to 16% of all fatal crashes in 2008 [
14]. Secondary tasks such as cellphone use have a high prevalence among drivers and can significantly increase driving risk [
15,
16]. Guo et al. [
17] examined secondary task engagement by age group and showed that, compared to middle-aged drivers (35–65 years old), senior drivers (≥65 years old) were much less likely than others to be engaged in secondary tasks (40.49% vs. 51.68%) and cellphone use (0.87% vs. 5.30%). However, the relative risk associated with distraction was much higher, with OR = 1.71 versus 1.58 for overall distraction and OR = 5.72 versus 2.11 for cellphone use. Examining whether arthritis plays a role in the prevalence and relative risk of distraction can improve the general understanding of the risk for senior drivers.
The majority of existing arthritis-related studies have relied on self-reported online surveys, or interviews or experiments with relatively small sample sizes. Large-scale NDSs enable researchers and policymakers to observe driver behavior and crash risk under real-life driving conditions [
18,
19]. The participants of an NDS will drive an instrumented vehicle with no specific instructions under real-life driving conditions for an extended period of time. The instrumented vehicles, typically the participants’ own, are equipped with a nonintrusive data acquisition system (DAS). The DAS is capable of recording comprehensive driving data, including GPS, radar, acceleration, and multi-channel video, capturing both the environment and the vehicle interior. Large NDSs are typically accompanied by surveys to gather demographic information, health conditions, crash history, and other information. The combination of comprehensive driving data and survey data provides an opportunity to draw insight into crash causation, driver risk factors, and potential safety countermeasures.
The inconsistency in the relationship between arthritic and crash risk, as well as the association between arthritis and other known risk factors, deserves in-depth research using novel data sources. In this study, we attempt to fill in the knowledge gap by utilizing the SHRP 2 NDS dataset to assess the impact of arthritic and crash risk, as well as the association of arthritis with other risk factors. The HPR 2 NDS is the largest NDS to date, with more than 3400 participants and 1 million hours of continuous driving data. The objectively collected naturalistic dataset provides accurate information about the moment the crashes happened and the driver’s behavior under normal driving conditions. The NDS data provide complementary evidence to studies based on self-reported crashes (Anstey et al. [
9] and Hong et al. [
20]). A comprehensive analysis was conducted to examine the association between arthritis and crash risk, secondary task engagement, driver personality, and fitness-drive information.
3. Dataset
This study used the SHRP 2 NDS dataset. The dataset includes 3563 participants from six US sites in Florida, Indiana, North Carolina, New York, Pennsylvania, and Washington. The participants’ vehicles were instrumented with an advanced DAS that collected dozens of key driving variables, including four-channel video views, GPS, three dimensions of acceleration, and the yaw rate. The driving data were continuously collected from ignition-on to ignition-off at various frequencies (e.g., videos at 10 Hz and GPS at 1 Hz). Each driver participated for 1 or 2 years, and the data were collected between 2010 and 2015 [
10].
In this study, we used two datasets: driver information survey results and driving events. The driver information survey was conducted at the time of recruitment, including age group, sex, personality traits such as the SSS, physical fitness, and medical conditions, including arthritis status. The SSS is a questionnaire-based assessment metric on a scale from 0 to 40, measuring the willingness of an individual to participate in new or intense situations. Individuals with high scores are likely to be sensation seekers. The participants also went through a series of physical exams that included metrics such as hand grip strength. A list of variables is presented in
Table 1.
The second dataset (i.e., the event dataset) provides unique insights into both the natural driving behaviors of drivers and the driving environments. The event dataset includes two types of events, crashes and control driving segments, or baseline epochs. The control driving segments are short (6 s) driving segments under normal, non-safety-critical conditions. The controls were randomly selected following a case-cohort approach, and can be used to evaluate the prevalence and risk associated with other variables [
18,
19]. Trained data reductionists reviewed the videos of a crash (5 s before the precipitating event of the crash until the end of the event) and annotated dozens of parameters related to driver behavior, traffic conditions, and driving environment following the protocol described in
SHRP 2 Researcher Dictionary for Video Reduction Data, Version 3.4 (
https://insight.shrp2nds.us (accessed on 10 October 2022)). The same data reduction protocol was used to extract information for around 20,000 randomly selected control driving segments.
6. Summary and Discussion
Arthritis can negatively affect driving safety and mobility, which especially impacts the senior driver population with a high prevalence of arthritis. Determining the impact of arthritis on crash risk and driver behavior could provide critical information to assess the fitness-to-drive of senior drivers, and aid in the development of driving assistance systems. Using the largest naturalistic study to date, the SHRP 2 NDS, this paper provides insights into the relationship between arthritis and crash risk, and secondary task engagement, personality traits, and physical fitness-to-drive factors.
The results show that arthritis is significantly associated with crash risk. Specifically, the crash risk for the drivers with arthritis was twice that for the arthritis-free drivers. This significant effect has been adjusted for age and sex factors through the mixed-effect logistic regression model. The finding indicates that arthritis does increase driving risk, even after the consideration of age, a known confounding factor. There is a need to explore the causal path for increased risk (i.e., the aspects of driving capability that arthritis impairs that lead to increased risk).
In this study, we took the first step to reveal the aforementioned causal path by assessing the impact of arthritis on secondary task engagement, sensation-seeking, and right-hand grip strength. The results indicate that there is no significant association between arthritis and secondary task engagement after controlling for age. Therefore, the increased crash risk among drivers with arthritis is not likely to be linked to secondary task engagement. Similarly, there is no evidence that arthritis is associated with SSS. Unlike previous studies, right-hand grip strength was not found to be weak for drivers with arthritis, and thus, there is no evidence that grip strength is a cause for the increased crash risk.
Using the novel, objectively collected NDS data, this study provided evidence in support of the significant association between arthritis and crash risk. We hypothesize that the root reason for the elevated risk could be the limited range of joint motion or pain, which impairs the safe operation of the vehicle or leads to a slower response to safety-critical situations. Additional in-depth studies with properly measured information must be conducted to identify the causal pathway for the increased crash risk, and ultimately to develop driving assistant systems to improve the safety of drivers with arthritis.